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    "cells": [
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "YC4R6nkCp91d",
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/"
                },
                "id": "YC4R6nkCp91d",
                "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
                "tags": []
            },
            "outputs": [],
            "source": [
                "# download files\n",
                "!mkdir data\n",
                "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
                "!unzip data/UBER.zip -d data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "c1df01cb-ad50-44b8-9f36-5a5788b498df",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "import nest_asyncio\n",
                "\n",
                "nest_asyncio.apply()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
            "metadata": {
                "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
            },
            "outputs": [],
            "source": [
                "from llama_index import download_loader, VectorStoreIndex\n",
                "from pathlib import Path"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
            "metadata": {
                "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
                "tags": []
            },
            "source": [
                "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
                "\n",
                "Leverage the capabilities of Unstructured.io HTML parsing.\n",
                "Downloaded through LlamaHub."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "years = [2022, 2021, 2020, 2019]"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
            "metadata": {
                "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
            },
            "outputs": [],
            "source": [
                "UnstructuredReader = download_loader(\n",
                "    \"UnstructuredReader\", refresh_cache=True, use_gpt_index_import=True\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "29084a11-f3da-428d-ae17-27b85a365e84",
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/"
                },
                "id": "29084a11-f3da-428d-ae17-27b85a365e84",
                "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
            },
            "outputs": [],
            "source": [
                "loader = UnstructuredReader()\n",
                "doc_set = {}\n",
                "all_docs = []\n",
                "for year in years:\n",
                "    year_docs = loader.load_data(\n",
                "        file=Path(f\"./data/UBER/UBER_{year}.html\"), split_documents=False\n",
                "    )\n",
                "    # insert year metadata into each year\n",
                "    for d in year_docs:\n",
                "        d.metadata = {\"year\": year}\n",
                "    doc_set[year] = year_docs\n",
                "    all_docs.extend(year_docs)"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
            "metadata": {
                "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
            },
            "source": [
                "### Setup a Vector Index for each SEC filing\n",
                "\n",
                "We setup a separate vector index for each SEC filing from 2019-2022.\n",
                "\n",
                "We also optionally initialize a \"global\" index by dumping all files into the vector store."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
            "metadata": {
                "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
                "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
            },
            "outputs": [],
            "source": [
                "from llama_index.indices.service_context import ServiceContext\n",
                "\n",
                "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
                "# initialize simple vector indices + global vector index\n",
                "# NOTE: don't run this cell if the indices are already loaded!\n",
                "index_set = {}\n",
                "for year in years:\n",
                "    cur_index = VectorStoreIndex.from_documents(\n",
                "        doc_set[year], service_context=service_context\n",
                "    )\n",
                "    index_set[year] = cur_index"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
            "metadata": {
                "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
            },
            "source": [
                "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
                "\n",
                "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a Summary index\n",
                "on top of the 4 vector indices."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
            "metadata": {
                "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
            },
            "outputs": [],
            "source": [
                "from llama_index import SummaryIndex, LLMPredictor\n",
                "from llama_index.llms import OpenAI\n",
                "from llama_index.composability import ComposableGraph"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
            "metadata": {
                "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
            },
            "outputs": [],
            "source": [
                "# set summary text for each doc\n",
                "index_summaries = {}\n",
                "for year in years:\n",
                "    index_summaries[year] = f\"UBER 10-k Filing for {year} fiscal year\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
            "metadata": {
                "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
            },
            "outputs": [],
            "source": [
                "# set number of output tokens\n",
                "llm = OpenAI(temperature=0, max_tokens=512)\n",
                "service_context = ServiceContext.from_defaults(llm=llm)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
            "metadata": {
                "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
                "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
            },
            "outputs": [],
            "source": [
                "# define a summary index over the vector indices\n",
                "# allows us to synthesize information across each index\n",
                "graph = ComposableGraph.from_indices(\n",
                "    SummaryIndex,\n",
                "    children_indices=[index_set[y] for y in years],\n",
                "    index_summaries=index_summaries,\n",
                "    service_context=service_context,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "30803f70",
            "metadata": {},
            "outputs": [],
            "source": [
                "query_engine = graph.as_query_engine(response_mode=\"tree_summarize\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "2f23ba33-416f-433a-9e83-5a37ed060344",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "import asyncio\n",
                "import time\n",
                "\n",
                "cross_query_str = \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
                "\n",
                "start_time = time.perf_counter()\n",
                "task = query_engine.aquery(cross_query_str)\n",
                "response = asyncio.run(task)\n",
                "elapsed_time = time.perf_counter() - start_time"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "716c9452-ef9f-4105-a4ad-5d72ad42f94e",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "print(str(response))\n",
                "print(str(elapsed_time))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "38842da1",
            "metadata": {},
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